Zero sequence impedance detection method based on digital twinning and related device

Abstract

This application provides a zero-sequence impedance detection method and related apparatus based on digital twins, applied to the main server of a distribution network system. The distribution network system further includes: detection modules at the first and second ends of a dual-circuit transmission line; a frequency sweep excitation module communicatively connected to the main server, and electrically connected to the first end. The method includes: acquiring environmental data and line structure data of the distribution network system; controlling the frequency sweep excitation module to send a frequency sweep excitation signal varying within a preset frequency band to the distribution network system; controlling the detection module to detect the zero-sequence detection signal of the distribution network system in response to the frequency sweep excitation signal; receiving the zero-sequence detection signal; determining zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal; and inputting the environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into a preset digital twin model to obtain zero-sequence impedance parameters. By implementing the embodiments of this application, the impedance detection accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a zero-sequence impedance detection method and related apparatus based on digital twins. Background Technology

[0002] With the continuous expansion of power system scale and the increasing complexity of power grid structure, double-circuit lines, as an important component of the power system, have crucial zero-sequence mutual impedance parameters that are essential for power system protection settings, fault analysis, and operational safety. Accurately obtaining the zero-sequence mutual impedance parameters of double-circuit lines is vital for improving the reliability and safety of power grid operation. Currently, the main methods for measuring the zero-sequence mutual impedance of double-circuit lines include the power frequency interference method, the zero-sequence incremental method, and the inter-frequency excitation method. Among these, the inter-frequency excitation method is widely used because it can effectively avoid power frequency interference and achieve zero-sequence mutual impedance measurement under conditions of partial power outage.

[0003] However, existing methods for measuring zero-sequence mutual impedance of double-circuit lines still have the following problems: First, the fixed-frequency excitation method is difficult to cope with the complex and ever-changing electromagnetic environment. If the interference happens to be near the excitation frequency, the measurement error will increase significantly, affecting the accuracy and reliability of the measurement results. Second, traditional methods rely on manually establishing and solving the zero-sequence network equations, which is cumbersome and easily affected by factors such as capacitance current. Although compensation is achieved through averaging algorithms, the accuracy is still limited and cannot meet the requirements for high-precision measurement. Third, the existing measurement process cannot be online and automated, and still requires manual intervention and offline verification. This method is not only inefficient, but also cannot meet the requirements of smart grids for real-time perception of line parameter status, and cannot support the intelligent operation and management of the power grid. Fourth, there is a lack of adaptability to changes in environmental factors. Changes in environmental factors such as temperature and humidity can cause dynamic changes in line parameters, and existing methods cannot capture these changes in real time and make corresponding adjustments.

[0004] Therefore, there is an urgent need for a method to detect the zero-sequence impedance of a double-circuit line that can solve the above problems, reduce interference from factors such as capacitive current, and improve measurement accuracy. Summary of the Invention

[0005] This application provides a method and related apparatus for zero-sequence impedance detection based on digital twins, which improves the accuracy of zero-sequence mutual impedance detection.

[0006] In a first aspect, embodiments of this application provide a zero-sequence impedance detection method based on digital twins, applied to a main server of a power distribution network system. The power distribution network system further includes: a detection module disposed on a first end and a second end of a transmission line in the power distribution network system, wherein the first end is the current input position of the transmission line, and the second end is the current output position of the transmission line; and a frequency sweep excitation module communicatively connected to the main server, wherein the frequency sweep excitation module is electrically connected to the first end. The method includes:

[0007] Obtain environmental data and line structure data of the power distribution network system;

[0008] The frequency sweep excitation module is controlled to send a frequency sweep excitation signal with a preset frequency band to the power distribution network system;

[0009] The detection module is controlled to detect the zero-sequence detection signal of the power distribution system in response to the frequency sweep excitation signal;

[0010] Receive the zero-sequence detection signal and determine the zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal;

[0011] The environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data are input into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system.

[0012] Secondly, embodiments of this application provide a zero-sequence impedance detection device based on digital twins, applied to the main server of a distribution network system. The distribution network management system further includes: a detection module disposed at the beginning and end of a dual-circuit in the distribution network system; and a frequency sweep excitation module communicatively connected to the main server, wherein the output terminal of the frequency sweep excitation module is connected to the beginning of the dual-circuit in the distribution network system. The device includes:

[0013] The acquisition unit is used to acquire environmental data and line structure data of the power distribution network system;

[0014] The control unit is used to control the frequency sweeping excitation module to send a frequency sweeping excitation signal with a preset frequency band to the power distribution network system; and to control the detection module to detect the zero-sequence detection signal of the power distribution network system in response to the frequency sweeping excitation signal, and send the zero-sequence detection signal to the main server.

[0015] A determining unit is configured to receive the zero-sequence detection signal and determine zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal;

[0016] The calculation unit is used to input the environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system.

[0017] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in any method of the first aspect of this application.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0019] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0020] By implementing the embodiments of this application, the following beneficial effects are achieved:

[0021] This application provides a zero-sequence impedance detection method and related apparatus based on digital twin, applied to the main server of a distribution network system. The distribution network system includes: detection modules at the first and second ends of a dual-circuit transmission line, and a frequency sweep excitation module communicatively connected to the main server, with the frequency sweep excitation module electrically connected to the first end. The method includes: acquiring environmental data and line structure data of the distribution network system; controlling the frequency sweep excitation module to send a frequency sweep excitation signal varying within a preset frequency band to the distribution network system; controlling the detection module to detect the zero-sequence detection signal of the distribution network system in response to the frequency sweep excitation signal; receiving the zero-sequence detection signal; determining zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal; and inputting the environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into a preset digital twin model to obtain zero-sequence impedance parameters. Thus, by integrating a digital twin model of temperature, humidity, and line structure parameters, the mapping between the physical entity and the virtual model of the power equipment is realized, thereby reducing the interference of environmental factors on the detection accuracy. In addition, by applying a variable frequency excitation signal and collecting response data, instead of constructing equations and solving them, interference from factors such as capacitance current is effectively reduced, thereby improving the detection accuracy of impedance. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is an architecture diagram of a zero-sequence impedance detection system based on digital twin provided in an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0025] Figure 3 This is a schematic flowchart of a zero-sequence impedance detection method based on digital twin provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the structure of a dual-loop zero-sequence impedance detection system provided in an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of a process for constructing a digital twin model provided in an embodiment of this application;

[0028] Figure 6 This is a schematic flowchart of another zero-sequence impedance detection method based on digital twin provided in the embodiments of this application;

[0029] Figure 7 This is a functional module block diagram of a zero-sequence impedance detection device based on digital twin provided in an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0031] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0032] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0033] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0034] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0035] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0036] The following is an explanation of the relevant terms used in this application:

[0037] Zero-sequence impedance: In a three-phase power system, the impedance presented when zero-sequence currents of equal magnitude and phase flow through the three-phase windings. It consists of zero-sequence resistance and zero-sequence reactance (including zero-sequence self-impedance and zero-sequence mutual impedance), reflecting the system's resistance to zero-sequence current. It is a parameter for power system zero-sequence protection setting and ground fault analysis.

[0038] To address the technical problem of low measurement accuracy in existing zero-sequence mutual impedance measurement methods for double-circuit transmission lines, and to achieve strong anti-interference capability, high measurement accuracy, online monitoring, and automation and intelligence, this application provides a zero-sequence impedance detection method and related device based on digital twins. This method is applied to the main server of a distribution network system. The distribution network system includes: detection modules at the first and second ends of the double-circuit transmission line; a frequency sweep excitation module communicatively connected to the main server; and the frequency sweep excitation module electrically connected to the first end. The method includes: acquiring environmental data and line structure data of the distribution network system; controlling the frequency sweep excitation module to send a frequency sweep excitation signal varying within a preset frequency band to the distribution network system; controlling the detection module to detect the zero-sequence detection signal in response to the frequency sweep excitation signal; receiving the zero-sequence detection signal; determining zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal; and inputting the environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into a preset digital twin model to obtain zero-sequence impedance parameters. Thus, by integrating a digital twin model of temperature, humidity, and line structure parameters, the mapping between the physical entity and the virtual model of the power equipment is realized, thereby reducing the interference of environmental factors on the detection accuracy. In addition, by applying a variable frequency excitation signal and collecting response data, instead of constructing equations and solving them, interference from factors such as capacitance current is effectively reduced, thereby improving the detection accuracy of impedance.

[0039] The following is combined Figure 1 The system architecture of a zero-sequence impedance detection method based on digital twins in the embodiments of this application is described below. Figure 1 This is an architecture diagram of a zero-sequence impedance detection system based on digital twin provided in an embodiment of this application. The zero-sequence impedance detection system 100 based on digital twin includes a digital twin analysis platform 110, a frequency sweep excitation unit 120, a distributed intelligent sensor network 130, and a control and communication unit 140.

[0040] The digital twin analysis platform 110 is used to construct a multi-physics coupled digital twin model of the double-circuit line and to accurately solve and dynamically correct the zero-sequence impedance parameters based on multi-source input data. Its functions include the fusion analysis of historical operating data, real-time simulation of the virtual model, comparison of the state of the physical entity and the virtual model, and parameter calibration, ultimately outputting the zero-sequence self-impedance and mutual impedance parameters of the double-circuit line. In one possible embodiment, the digital twin analysis platform 110 incorporates a simulation model based on the electromagnetic transient theory of power systems. The model input dimensions include the structural parameters of the double-circuit line (conductor type, line spacing, tower type), spatial relative position, real-time environmental data (temperature and humidity), and historical zero-sequence operating data. By comparing the real-time data collected by the distributed intelligent sensor network 130 with the theoretical data output by the simulation model in real time, the least squares method is used to dynamically correct parameters such as line resistance and reactance in the model, ensuring that the virtual model and the physical entity of the double-circuit line maintain spatiotemporal consistency, thereby eliminating the interference of environmental factors on the accuracy of zero-sequence impedance detection.

[0041] The frequency sweep excitation unit 120 is used to inject a preset frequency band of frequency sweep excitation signal into the de-energized line in the double-circuit power distribution network to excite the zero-sequence response of the double-circuit line. The output terminal of the frequency sweep excitation unit 120 is electrically connected to the beginning of the double-circuit line (power supply side connection terminal) through an isolation transformer to avoid electromagnetic interference caused by the excitation signal to the normal power supply of the operating line. In one possible embodiment, the frequency sweep excitation unit 120 is constructed using direct digital frequency synthesis (DDS) technology, which can generate a continuous linear frequency sweep signal in the 40Hz-60Hz frequency band with a frequency sweep step accuracy of not less than 0.1Hz. Its output power can be adaptively adjusted according to the impedance characteristics of the double-circuit line, and it has overcurrent and overvoltage protection functions to ensure the electrical safety of the excitation process. At the same time, the frequency, amplitude, and duration of the frequency sweep signal can be remotely configured by the control and communication unit 140 to adapt to the detection scenarios of double-circuit lines with different voltage levels and line lengths.

[0042] The distributed intelligent sensor network 130 is used to synchronously acquire zero-sequence voltage and zero-sequence current signals at the beginning and end of the double-circuit line, and to perform edge computing preprocessing on the raw signals. The network consists of multiple sets of intelligent sensing nodes deployed at the beginning (power supply side) and end (load side) of the double-circuit line, with each set of nodes corresponding to one end position of one of the lines in the double-circuit line. In one possible embodiment, each set of intelligent sensing nodes integrates a high-precision voltage transformer (accuracy class 0.2S), a high-precision current transformer (accuracy class 0.2S), a GPS / BeiDou synchronization clock module (synchronization accuracy ≤1μs), an embedded microprocessor, and a wireless communication module. The nodes achieve microsecond-level synchronous acquisition of the signals at the beginning and end through the synchronization clock. The microprocessor sequentially performs outlier removal (moving average algorithm), bandpass filtering (40Hz-60Hz), 50Hz filtering, and fast Fourier transform (FFT) on the raw signals, converting the time-domain signals into zero-sequence voltage and current data in the frequency domain, and then uploading them to the control and communication unit 140 through the wireless communication module, effectively reducing data transmission bandwidth and improving the quality of input data.

[0043] The control and communication unit 140 is used to implement command issuance, data interaction, and status monitoring between the digital twin analysis platform 110, the frequency sweeping excitation unit 120, and the distributed intelligent sensor network 130. It includes receiving control commands from the digital twin analysis platform 110, scheduling the signal output parameters of the frequency sweeping excitation unit 120, summarizing preprocessed data from the distributed intelligent sensor network 130, and uploading standardized datasets to the digital twin analysis platform 110. In one possible embodiment, the control and communication unit 140 uses an industrial-grade embedded controller, with communication interfaces supporting Ethernet (for communication with the digital twin analysis platform 110) and LoRa (for communication with intelligent sensor nodes). The communication link uses an encrypted transmission protocol to ensure data security. Furthermore, the control and communication unit 140 also has a status feedback function, which can monitor the output status of the frequency sweeping excitation unit 120 and the operating status of the intelligent sensor nodes in real time. When a device malfunctions, it sends alarm information to the digital twin analysis platform 110 to ensure the reliability of system operation. Furthermore, the communication interface of the control and communication unit 140 supports multiple communication protocols, including IEC61850, DNP3.0, and Modbus, ensuring compatibility with different power grid dispatching systems. The control and communication unit 140 also implements remote access and control functions, allowing maintenance personnel to configure system parameters and diagnose faults via an encrypted VPN connection.

[0044] Furthermore, the frequency sweep excitation unit 120 is electrically connected to the first end of the de-energized line of the double-circuit line through an isolation transformer to safely inject the frequency sweep excitation signal; each node of the distributed intelligent sensor network 130 is electrically connected to the conductors of the double-circuit line through a current transformer to avoid affecting the normal operation of the line; the digital twin analysis platform 110, the frequency sweep excitation unit 120, and the distributed intelligent sensor network 130 are all connected through the control and communication unit 140. During data processing, the preprocessed data of the distributed intelligent sensor network 130 is standardized by the control and communication unit 140 and input into the digital twin analysis platform 110 along with the real-time environmental data. The digital twin analysis platform 110 compares the input data with the simulation results of the virtual model and outputs the corrected zero-sequence impedance parameters. At the same time, it can issue commands to the control and communication unit 140 to adjust the signal parameters of the frequency sweep excitation unit 120 or trigger the resampling operation of the distributed intelligent sensor network 130. Thus, the collaborative work of each module in the digital twin-based zero-sequence impedance detection system 100 can effectively improve the detection accuracy and stability of zero-sequence impedance of double-circuit lines, provide reliable parameter support for ground fault protection setting and fault location in the distribution network, and significantly enhance the safe operation capability of the distribution network system.

[0045] The following is combined Figure 2 The electronic devices in the embodiments of this application will be described. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 2 As shown, the electronic device includes one or more processors 210, a memory 220, a communication interface 230, and one or more programs 221. The processor 210 is communicatively connected to the memory 220 and the communication interface 230 via an internal communication bus.

[0046] The one or more programs 221 are stored in the memory 220 and configured to be executed by the processor 210. The one or more programs 221 include instructions for performing any step in the above method embodiments.

[0047] The processor 210 can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.

[0048] The memory 220 can be volatile memory or non-volatile memory, or it can include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0049] It is understood that the electronic device 200 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device 200 may be equipped with... Figure 1 The architecture of the zero-sequence impedance detection system based on digital twins.

[0050] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 3 This application describes a zero-sequence impedance detection method based on digital twins. Figure 3 This is a flowchart illustrating a zero-sequence impedance detection method based on digital twins provided in an embodiment of this application. The method is applied to a main server of a power distribution network system. The power distribution network system further includes: detection modules disposed at a first end and a second end of a transmission line in the power distribution network system, wherein the first end is the current input position of the transmission line and the second end is the current output position of the transmission line; and a frequency sweep excitation module communicatively connected to the main server, and the frequency sweep excitation module is electrically connected to the first end. The method specifically includes the following steps:

[0051] Step S310: Obtain environmental data and line structure data of the power distribution network system.

[0052] Among them, environmental data refers to the impact of external operating conditions on the zero-sequence parameters of the distribution network. This environmental data includes temperature, humidity, and rainfall, and may also include parameters that affect the insulation status of the line and the characteristics of the conductor, such as ice thickness, wind speed, wind direction, and the surrounding geographic electromagnetic environment. These are not limited here.

[0053] Specifically, the line structure data refers to the structural influence of the power distribution line itself on the zero-sequence impedance. This line structure data includes conductor type, conductor material, as well as structural parameters such as tower spacing, phase line arrangement, tower grounding resistance, line corridor length, cable / overhead line type information, line laying method, and phase-to-phase distance, which are not limited here.

[0054] It is evident that by considering environmental data and line structure data, the input data of the model can be enriched, enabling the digital twin model to learn more information about the distribution network system, thereby improving the fitting accuracy of zero-sequence impedance changes and allowing the model to maintain accurate modeling capability of zero-sequence parameters under conditions of external environmental changes and line structure differences.

[0055] It should be noted that environmental data and line structure data are obtained through real-time collection by the distribution network system, retrieval from historical databases, or uploading from on-site monitoring equipment. The distribution network system can only input the data into the digital twin model after performing time synchronization and data quality verification to avoid abnormal data causing deviations in the calculation results of zero-sequence impedance parameters.

[0056] Step S320: Control the frequency sweep excitation module to send a frequency sweep excitation signal with a preset frequency band variation to the power distribution network system.

[0057] The frequency sweep excitation module generates a frequency sweep signal with controlled amplitude and continuously varying frequency within the target frequency band to simulate the response characteristics of zero-sequence branches in the distribution network at different frequencies. The preset frequency band ranges from 40Hz to 60Hz, and the frequency variation method is either continuous linear frequency sweep or adaptive step frequency sweep. Continuous linear frequency sweep refers to the frequency increasing linearly from 40Hz to 60Hz at a constant rate; while adaptive step frequency sweep dynamically adjusts the frequency step size according to the response characteristics in the dual-loop line, using a smaller frequency step size near key frequency points to obtain more accurate response data.

[0058] Specifically, the frequency sweep excitation signal changes its signal frequency in a linear or logarithmic manner within a selected frequency band while maintaining a stable excitation amplitude output, so that the distribution network system generates corresponding zero-sequence voltage and zero-sequence current responses at different frequency points, which can be used for subsequent feature extraction and impedance calculation.

[0059] It is evident that by employing a frequency sweep excitation method within a preset frequency band, the zero-sequence characteristics of the distribution network can be displayed point by point throughout the entire frequency range. This is beneficial for capturing the impedance variation law of the line grounding channel under multiple frequency bands, thereby improving the fitting accuracy and stability of the digital twin model for the zero-sequence impedance parameters.

[0060] Step S330: Control the detection module to detect the zero-sequence detection signal of the power distribution system in response to the frequency sweep excitation signal.

[0061] The detection module is used to accurately measure the zero-sequence response of lines and related equipment when a frequency sweep excitation signal is applied to the distribution network system. It also performs synchronous sampling, data demodulation, and frequency domain feature extraction of voltage and current responses at different frequency points to ensure that zero-sequence detection data strictly corresponds to the excitation frequency is obtained. This detection process not only needs to ensure high accuracy and high sampling rate for voltage and current measurements, but also needs to guarantee time synchronization and phase consistency to avoid the accumulation of errors in subsequent zero-sequence impedance parameter calculations due to sampling deviations.

[0062] Specifically, detection modules are deployed at both ends of the dual-circuit line. These modules include multiple circuit sensors and voltage sensors to collect the induced zero-sequence current and zero-sequence voltage response data of the sweep frequency excitation signal on the operating line. Each sensor integrates a microprocessor, which performs local preprocessing and feature extraction on the collected zero-sequence current and voltage data before uploading the processed data. The microprocessor's preprocessing includes data filtering, outlier detection, and Fourier transform operations to reduce transmission bandwidth requirements and improve data quality. Preferably, the detection module consists of zero-sequence voltage sensors, zero-sequence current sensors, transient measurement devices (PMU / µPMU), and a signal digital processing unit deployed at key busbars, branch line ends, and fault-sensitive nodes in the distribution network. When detecting the response data of the sweep frequency excitation signal, the detection module first collects the zero-sequence voltage and zero-sequence current waveforms generated during the sweep frequency excitation injection in real time. Since the sweep frequency excitation continuously changes within a preset frequency band, the detection module needs to maintain a high sampling density at each instantaneous frequency to accurately capture the amplitude changes, phase shifts, and harmonic characteristics of the system response. After data acquisition, the detection module further utilizes its internal digital signal processing unit to preprocess the acquired voltage and current signals, including bandpass filtering, noise reduction, normalization, and synchronous reference frame transformation, ensuring that only effective components related to the current excitation frequency are retained in the signal. Then, the detection module performs frequency domain analysis on the preprocessed signal, such as Fast Fourier Transform (FFT), phase demodulation, and amplitude extraction, to obtain the zero-sequence voltage amplitude, zero-sequence current amplitude, and the phase difference between them at the corresponding frequency point. Furthermore, to further improve the reliability of the detection data, the detection module can also perform cross-node data consistency verification. For example, by comparing and fusing zero-sequence measurement data from neighboring nodes, anomalies caused by transient disturbances, communication jitter, or sensor drift in the acquired data can be identified and removed, thereby ensuring data consistency in the spatial dimension.

[0063] For easier understanding, please refer to Figure 4 , Figure 4This is a schematic diagram of a dual-circuit zero-sequence impedance detection system provided in an embodiment of this application. As can be seen, the dual-circuit zero-sequence impedance detection system 400 uses a server as the analysis node and integrates a frequency sweep excitation module and a distributed detection module, enabling synchronous acquisition of zero-sequence signals and impedance parameter calculation at the beginning and end of the dual-circuit line. Specifically, the dual-circuit zero-sequence impedance detection system 400 includes: a server, a frequency sweep excitation module, and four sets of detection modules. The detection modules are deployed at the beginning and end of the dual-circuit lines (Line 1 and Line 2): one set of detection modules is deployed at each end of Line 1, and one set of detection modules is deployed at each end of Line 2, forming a monitoring system for the electrical nodes of the dual-circuit lines. The server, acting as the system's control and data processing hub, interacts with the frequency sweep excitation module via a communication link. It sends frequency sweep parameter configurations (such as frequency band range and amplitude) to the module and receives zero-sequence signal data uploaded by the detection module. The output of the frequency sweep excitation module is electrically connected to the beginning of line 1 of the double-circuit line, injecting a preset frequency sweep excitation signal into line 1. This signal, through the electromagnetic coupling effect of the double-circuit line, induces a zero-sequence response signal in the adjacent line 2, thus avoiding direct electrical interference with the operating line. All four detection modules are electrically connected to the conductors of their respective lines via current transformers in a non-contact manner. The detection modules at the beginning and end of line 1 collect the zero-sequence voltage and current signals of line 1 under frequency sweep excitation; the detection modules at the beginning and end of line 2 collect the zero-sequence voltage and current signals induced in line 2. All detection modules have synchronous time synchronization capabilities (such as GPS / BeiDou clock synchronization), ensuring consistency in the time base of signal acquisition at both ends and eliminating phase errors caused by signal transmission delays. When the dual-circuit zero-sequence impedance detection system 400 is running, the server sends a frequency sweep command to the frequency sweep excitation module, which then injects a frequency sweep excitation signal into the beginning of line 1. Simultaneously, four detection modules synchronously start acquiring data, converting the zero-sequence signals from the beginning and end of lines 1 and 2 into digital quantities, and uploading them to the server via the communication link. After receiving the data, the server performs frequency domain analysis (such as Fourier transform) on the signals from the beginning and end, extracting the zero-sequence voltage and current amplitude and phase information within the effective frequency band. Finally, based on the impedance calculation model, it solves for the zero-sequence self-impedance and mutual impedance parameters of the dual-circuit lines. The dual-circuit zero-sequence impedance detection system 400's system architecture, through hardware deployment at the physical layer and synchronous control at the logic layer, effectively avoids the impact of capacitive current and environmental interference on detection accuracy in traditional methods, improving the reliability of zero-sequence impedance parameter detection for dual-circuit lines. Furthermore, the signal source output is connected to the de-energized line via an isolation transformer. The isolation transformer is made of high-permeability iron core material, possessing excellent low-frequency characteristics, which can effectively isolate the measurement equipment from the high voltage of the power grid while ensuring signal transmission quality, thus improving system safety.The primary winding of the isolation transformer is connected to the output terminal of the signal source, while the secondary winding is connected to the power outage line. The transformation ratio can be adjusted according to the actual engineering requirements, usually using a transformation ratio of 1:10 or 1:20 to ensure that the injected signal has sufficient strength.

[0064] It is evident that by controlling the detection module to accurately detect the zero-sequence detection signal of the distribution network system in response to the frequency sweep excitation signal, not only can the electrical characteristics of the line be comprehensively reflected from the frequency domain perspective, but a structured dataset of the responses of zero-sequence voltage and zero-sequence current at different frequency points can also be established, providing a high-quality input data foundation for subsequent identification of zero-sequence impedance parameters. Since the essence of frequency sweep excitation is to gradually excite the real electrical characteristics of the distribution network at different frequency points, the measured zero-sequence detection signal can truly reflect the impact of factors such as line structure, environmental changes, grounding status, and equipment parameter changes on the zero-sequence channel, making subsequent digital twin calculations more accurate and reliable.

[0065] In one possible embodiment, the detection module includes a sensor and a communication module. Controlling the detection module to detect the zero-sequence detection signal of the power distribution system in response to the frequency sweep excitation signal specifically includes the following steps:

[0066] 331. The sensor detects the first response signal at the beginning of the dual-circuit of the power distribution network system and the second response signal at the end of the dual-circuit of the power distribution network system.

[0067] 332. Determine the first detection signal based on the first response signal and the second response signal;

[0068] 333. An anomaly detection is performed on the first detection signal based on a preset anomaly detection algorithm to obtain an anomaly signal;

[0069] 334. Determine the second detection signal based on the first detection signal and the abnormal signal;

[0070] 335. The second detection signal is filtered based on a preset filtering algorithm to obtain a filtered detection signal;

[0071] 336. Perform a Fourier transform on the filtered detection signal to obtain a zero-sequence detection signal;

[0072] 337. The zero-sequence detection signal is sent to the main server through the communication module.

[0073] The system includes sensors such as a zero-sequence voltage sensor, a zero-sequence current sensor, and a synchronous sampling unit for high-precision measurement. These sensors synchronously acquire voltage and current changes at different nodes in the dual-loop circuit under the influence of a frequency sweep excitation signal. The first detection signal is obtained by performing amplitude calculation, phase analysis, and differential operations on the response signals at both ends to reflect the comprehensive electrical response of the dual-loop system to each frequency sweep point. An anomaly detection algorithm identifies data anomalies caused by sensor drift, short-term interference, transient instability, electromagnetic interference, etc. Algorithms can include isolated forest algorithms, Z-score statistical detection methods, threshold deviation identification models, etc. The second detection signal is obtained by removing anomalies from the first detection signal or by interpolating and correcting the anomaly segments, ensuring the integrity and continuity of the data sequence. A filtering algorithm further removes high-frequency noise, low-frequency drift, and harmonic interference. Bandpass filters, wavelet denoising, or adaptive filtering algorithms can be used. The communication module can employ fiber optic communication, 5G, LoRa, or a dedicated power distribution communication network for stable transmission of large-scale frequency domain characteristic data.

[0074] Specifically, when the frequency sweep excitation module injects a continuously varying frequency sweep excitation signal into the double-circuit distribution network, the first and last ends will generate corresponding zero-sequence responses. The sensors, based on a high sampling rate mode, capture the transient waveforms at both nodes and convert them into a first response signal and a second response signal. Then, by performing time alignment and phase synchronization correction on the two sets of response signals, and according to a preset signal fusion algorithm, the response curves of the two nodes are mapped to corresponding frequency points to obtain a first detection signal representing the entire line. The amplitude sequence, phase sequence, and their changing trends of the first detection signal are detected. Points that significantly deviate from the normal response pattern are marked as abnormal signals, and the abnormality category and frequency location are recorded. For abnormal points, local regression interpolation, spline interpolation, or neighborhood smoothing can be used for substitution; for anomalies over long periods, dual-end data reconstruction can be directly used to generate a second detection signal that is anomaly-free and continuous. Then, the filter passband is automatically set according to the target frequency band of the frequency sweep excitation, retaining only the effective components corresponding to the frequency sweep frequency, thereby constructing a high signal-to-noise ratio filtered detection signal. A Fast Fourier Transform (FFT) is then performed to map the time-domain filtered detection signal to the frequency domain, extracting the amplitude, phase, and spectral characteristics of each frequency point, thus obtaining the zero-sequence detection signal for subsequent modeling. Finally, FFT is performed on the filtered voltage and current waveforms respectively, and the amplitude-phase ratio is calculated to obtain zero-sequence electrical response data corresponding one-to-one with the frequency sweep frequency points. After data packet encapsulation, encryption, and link verification, the communication module transmits the zero-sequence detection signal to the main server in real time to support the dynamic updating of the digital twin model and the calculation of zero-sequence impedance parameters.

[0075] It is evident that by acquiring, eliminating anomalies, optimizing filtering, and extracting frequency domain signals from the dual-circuit zero-sequence response signals of the distribution network, the obtained zero-sequence detection signals possess high signal-to-noise ratio, high accuracy, and high stability. Furthermore, this improves the reliability of zero-sequence impedance parameter identification, providing a solid data foundation for the accurate construction of digital twin models, thereby enhancing the intelligent capabilities of distribution network fault diagnosis, anomaly detection, and operational assessment.

[0076] Step S340: Receive the zero-sequence detection signal and determine the zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal.

[0077] The zero-sequence detection signal is a set of frequency domain signals generated by acquiring, filtering, correcting anomalies, and performing Fourier transforms on the electrical response of the dual-loop circuit under frequency sweep excitation. It contains zero-sequence voltage and zero-sequence current characteristics at different frequency points. After receiving the zero-sequence detection signal, it is necessary to further decompose, reconstruct, and extract the zero-sequence voltage and zero-sequence current components to meet the input structure requirements of the digital twin model.

[0078] Specifically, in the server or digital twin analysis platform, the received zero-sequence detection signals undergo data classification and channel parsing. By identifying the message tags, channel numbers, frequency sequences, and measurement type identifiers sent by the detection module, voltage and current responses on different channels are distinguished. For detection signals containing voltage measurement results, zero-sequence voltage data is directly constructed using frequency domain amplitude and phase characteristics; for detection signals containing current measurement results, zero-sequence current data is formed by calculating the amplitude and phase spectra of the current. Simultaneously, a preset synchronization calibration mechanism ensures strict alignment of voltage and current data at the same frequency point, avoiding phase deviations caused by sampling delays or communication time differences, thus guaranteeing the accuracy of subsequent zero-sequence impedance calculations. To improve data reliability, the received zero-sequence detection signals can also be quality-assessed based on signal quality indicators sent by the detection module (such as signal-to-noise ratio, amplitude abrupt change characteristics, and phase jump conditions), and abnormal data points that may be caused by communication noise, transient disturbances, or node load fluctuations can be removed or corrected. For example, when the amplitude of the zero-sequence detection signal at a certain frequency point deviates significantly from the historical statistical range, the system will automatically use data from neighboring frequencies for interpolation and repair; when the phase difference between voltage data and current data does not conform to physical laws, the system will perform synchronization correction according to the frequency sweep excitation law.

[0079] It is evident that by classifying, analyzing, assessing, calibrating, and normalizing the received zero-sequence detection signals, the server or digital twin analysis platform can accurately extract zero-sequence voltage and current data that reflect the true electrical characteristics of the line from complex frequency domain signals, providing a high-quality data foundation for subsequent key computational tasks such as digital twin model input, zero-sequence impedance characteristic analysis, fault mode identification, and parameter inversion.

[0080] In one possible embodiment, determining the zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal specifically includes the following steps:

[0081] 341. The zero-sequence detection signal is normalized to obtain a normalized detection signal;

[0082] 342. Extract the signal within a preset voltage frequency band from the normalized detection signal to obtain the zero-sequence voltage signal;

[0083] 343. Extract the signal within the preset current frequency band from the normalized detection signal to obtain the zero-sequence current signal;

[0084] 344. Demodulate the zero-sequence voltage signal and the zero-sequence current signal respectively to obtain zero-sequence voltage amplitude data, zero-sequence voltage phase data, zero-sequence current amplitude data, and zero-sequence current phase data;

[0085] 345. Determine the zero-sequence voltage data based on the zero-sequence voltage amplitude data and the zero-sequence voltage phase data;

[0086] 346. Determine the zero-sequence current data based on the zero-sequence current amplitude data and the zero-sequence current phase data.

[0087] Amplitude normalization aims to eliminate the offset effects caused by different detection devices, different sampling batches, or instantaneous line conditions on signal amplitude, ensuring that the signal amplitude falls within a uniform range, thereby guaranteeing the accuracy of subsequent frequency band extraction and amplitude analysis. Normalization can employ methods such as max-min normalization and Z-score normalization, and is not limited here.

[0088] Specifically, firstly, signals within preset voltage and current frequency bands are extracted from the normalized detection signal. Since the swept excitation signal generates different zero-sequence voltage and current responses at different frequency points when injected into the distribution network line, frequency band separation can effectively distinguish the voltage and current characteristics inherent in the same source signal. For example, a significant voltage response is generated in the 40Hz–50Hz range, while a significant current response is generated in the 50Hz–60Hz range. The signal is then bandpass extracted according to the frequency band interval. Digital filtering methods, such as FIR filters, IIR filters, or short-time Fourier window functions, are typically used to obtain zero-sequence voltage and zero-sequence current signals with good noise suppression and high frequency separation accuracy. Next, demodulation is performed on the zero-sequence voltage and zero-sequence current signals to obtain the two key features: amplitude and phase. Demodulation methods include envelope detection, synchronous demodulation, and Hilbert transform, used to restore the frequency domain signal to amplitude and phase sequences. Through demodulation, the amplitude response and phase angle changes of the swept signal at different frequency points can be accurately extracted. Then, by combining amplitude and phase data, zero-sequence voltage and zero-sequence current data are reconstructed. Zero-sequence voltage data typically consists of an amplitude spectrum, a phase spectrum, and a frequency sequence, providing a complete characterization of the line's zero-sequence voltage frequency response under frequency sweep excitation. Similarly, zero-sequence current data records the frequency domain amplitude and phase variations of the line's zero-sequence current, used for subsequent pairing with zero-sequence voltage data to calculate zero-sequence impedance. This reconstruction process generally employs a data structuring approach, matching amplitude and phase according to frequency points and constructing standardized input vectors that can be directly input into the digital twin model.

[0089] It is evident that comprehensive frequency domain processing, feature extraction, and data reconstruction of the zero-sequence detection signal significantly improves the extraction accuracy of zero-sequence voltage and current data, enabling them to more accurately reflect the intrinsic characteristics of distribution network lines. Through multi-level processing including normalization, frequency band separation, demodulation, and reconstruction, noise interference is effectively suppressed, improving data stability and reliability. This significantly enhances the accuracy and reliability of the digital twin model in tasks such as zero-sequence impedance parameter calculation, line condition assessment, and fault feature identification.

[0090] Step S350: Input the environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system.

[0091] The digital twin model, built upon multiphysics coupling theory, comprehensively describes the electrical characteristics and physical topology of a double-circuit line, including conductor material properties, tower parameters, spatial relative positions, and line geometry. By incorporating environmental data (such as temperature and humidity) and historical operational data, the digital twin model can dynamically correct for changes in line parameters caused by environmental factors, thereby improving the accuracy and reliability of zero-sequence impedance calculation. Furthermore, the model includes a built-in parameter adaptive module that iteratively optimizes internal parameters based on input zero-sequence voltage and current data, enabling the digital twin model to accurately reflect the zero-sequence characteristics under the current line conditions.

[0092] Specifically, the environmental data is first mapped to the environmental parameter nodes in the digital twin model. These nodes primarily simulate the impact of external environmental factors such as temperature, humidity, and air pressure on conductor resistance, reactance, and coupling capacitance. By inputting environmental data, the model can correct the line parameters based on physical formulas and empirical parameters, ensuring that the model's electrical characteristics remain consistent with actual operating conditions. Next, the line structure data is input to the model's topology node, which includes the spatial location of the double-circuit line, tower spacing, conductor arrangement, and branch information. Inputting this data ensures the model can correctly calculate the zero-sequence voltage and current distribution during simulation, thus reflecting the line's coupling and impedance characteristics. Then, the zero-sequence voltage and current data are input to the electrical parameter calculation module of the digital twin model. This module employs an intelligent algorithm combining deep learning and reinforcement learning, performing multi-level feature extraction and model fitting on the input data to accurately identify the zero-sequence self-impedance and zero-sequence mutual impedance of the double-circuit line. Internally, the model employs a dynamic optimization algorithm to compare the measured voltage and current responses with the model simulation results, automatically correcting line electrical parameters during the comparison process to further improve the accuracy of zero-sequence impedance calculation. Finally, in the zero-sequence impedance parameter output stage, the digital twin model generates a result vector from the calculated zero-sequence self-impedance and zero-sequence mutual impedance according to a preset format, providing frequency domain response curves, amplitude-phase diagrams, and impedance change trend graphs. These outputs not only allow maintenance personnel to intuitively understand the line status but also provide a basis for distribution network protection settings, fault analysis, and line status early warning. Furthermore, the model supports real-time updates and parameter self-adaptation, enabling timely adjustments to calculation results when the environment or line status changes, thereby maintaining the accuracy and dynamism of the zero-sequence impedance parameters.

[0093] It is evident that by inputting environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into the digital twin model for calculation, accurate identification and dynamic evaluation of the zero-sequence impedance of a double-circuit line can be achieved. This effectively overcomes the shortcomings of traditional zero-sequence network equation calculations, which are susceptible to environmental interference, complex to solve, and lack high accuracy. Furthermore, through intelligent optimization algorithms, the model parameters are corrected in real time, making the zero-sequence impedance detection results closer to the actual operating state. This provides reliable data support for the safe operation, fault analysis, and protection setting of the distribution network.

[0094] With the above Figure 3 For embodiments consistent with those described, please refer to [link / reference]. Figure 5 , Figure 5 This is a flowchart illustrating a process for constructing a digital twin model provided in an embodiment of this application. Before inputting the environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system, the method specifically includes the following steps:

[0095] S501. Obtain historical operating data of the power distribution network system;

[0096] S502, Determine the historical environmental data and historical zero-sequence operation data in the historical operation data;

[0097] S503. Construct a physical twin model based on the line structure data, the historical environment data, and the historical zero-sequence operation data to obtain a physical twin model;

[0098] S504. Input the historical operation data into the preset simulation model to obtain simulation data;

[0099] S505. Compare the simulation data and the historical running data to obtain a comparison result;

[0100] S506. Determine the difference parameters based on the comparison results;

[0101] S507. Input the difference parameters and the historical zero-sequence running data into the physical twin model to obtain the digital twin model.

[0102] Historical data includes zero-sequence voltage and current data of the double-circuit line at different operating times, as well as environmental conditions such as temperature, humidity, and wind speed. This historical operational data can be obtained through the distribution network's SCADA system or historical data acquisition devices, offering high temporal resolution and accuracy. Historical environmental data is used to simulate the impact of environmental changes on line parameters; for example, increased temperature may lead to increased conductor resistance, and changes in humidity may affect capacitive coupling between lines. Historical zero-sequence operational data includes the amplitude and phase of zero-sequence voltage and the amplitude and phase of zero-sequence current at each time point, characterizing the zero-sequence electrical characteristics of the double-circuit line. Line structural data includes conductor materials, tower spacing, spatial layout, and branch information. Combined with historical zero-sequence response data, a physical coupling dataset can be generated to simulate the actual electrical behavior of the double-circuit line under different environments. The physical twin model uses a multiphysics coupling algorithm to comprehensively map conductor resistance, inductance, capacitance, and environmental influencing factors, achieving preliminary prediction and response simulation of zero-sequence impedance.

[0103] Specifically, the historical operating data is first analyzed and classified to extract historical environmental data and historical zero-sequence operating data. Next, the historical operating data is input into a pre-set simulation model for simulation calculations, yielding simulation data. This pre-set simulation model employs power system theory and multiphysics modeling methods to accurately calculate the zero-sequence voltage and current response of a double-circuit line. The simulation data provides a theoretical reference for the digital twin model and is compared with historical actual operating data to evaluate the model's accuracy under different conditions. Preferably, the pre-set simulation model uses PSCAD / EMTDC for simulation. Then, amplitude and phase difference analysis is performed between the simulation data and the actual zero-sequence response to identify the deviations and error sources between the simulation results and actual measurements. Through difference analysis, the deviation between model predictions and actual operation can be quantified, providing a basis for subsequent model parameter adjustments. These difference parameters include amplitude and phase deviations of zero-sequence voltage and current, and adjustments to environmental factor influence coefficients, used to optimize the internal parameters of the digital twin model. The determination of difference parameters employs least squares, genetic algorithms, or other optimization algorithms to ensure that the adjusted model accurately fits historical operating characteristics. Finally, the difference parameters and historical zero-sequence operating data are input into the physical twin model to obtain the final digital twin model. In this way, the physical twin model not only retains the physical structure and electrical characteristics but also incorporates dynamic parameters corrected by historical data, achieving the adaptive capability and high-precision predictive function of the digital twin model.

[0104] It is evident that by constructing a digital twin model, high-precision dynamic calculation of the zero-sequence impedance of a dual-circuit line can be achieved, taking into account environmental factors and historical zero-sequence response characteristics.

[0105] In one possible embodiment, the step of constructing a physical twin model based on the line structure data, the historical environmental data, and the historical zero-sequence operation data to obtain a physical twin model specifically includes the following steps:

[0106] A1. Determine the physical topology and spatial location data of the distribution network system based on the line structure data;

[0107] A2. Couple the historical zero-sequence running data, the historical environment data, and the physical topology to obtain a physical coupling dataset;

[0108] A3. Generate the physical coupling model based on the physical coupling dataset and the spatial location data.

[0109] The line structure data includes conductor material, cross-sectional area, tower spacing, insulator type, branch information, and the spatial orientation of the line. By establishing mathematical models of power line nodes and branches, the system can generate a complete physical topology diagram, describing the node connections and branch distribution of the line. Simultaneously, by combining GPS or measurement coordinate information, it determines the relative positions of each node and tower in three-dimensional space, forming spatial location data.

[0110] Specifically, historical zero-sequence operational data, historical environmental data, and physical topology are coupled to obtain a physically coupled dataset. Historical zero-sequence operational data includes zero-sequence voltage amplitude, zero-sequence current amplitude, and corresponding phase information. Historical environmental data includes external factors that may affect the electrical characteristics of the line, such as temperature, humidity, and wind speed. Through a data fusion algorithm, the zero-sequence operational data is mapped to corresponding physical topology nodes and weighted in conjunction with environmental parameters to form the physically coupled dataset. This dataset comprehensively reflects the zero-sequence response characteristics of the line under different environmental conditions, enabling the physical model to reflect the dynamic changes in actual operation. Then, a multi-physics coupling modeling method is used to uniformly calculate the influence of conductor resistance, inductance, capacitance, and environmental factors, forming a physical model capable of simulating the zero-sequence response of a double-circuit line. The physically coupled model establishes electrical parameter matrices at nodes and branches, and performs electromagnetic coupling calculations in conjunction with spatial location data to ensure that the model reflects the actual zero-sequence electrical behavior of the line.

[0111] As can be seen, by constructing a physical twin model, the entire process from line structure analysis and historical data coupling to physical model generation is realized. The physical twin model can retain physical authenticity while incorporating environmental factors and historical zero-sequence characteristics, achieving multi-dimensional and multi-scenario simulation of zero-sequence electrical characteristics. Furthermore, this model provides a reliable foundation for subsequent training with historical operating data and differential parameters to generate a digital twin model, ensuring that the digital twin model accurately reflects the zero-sequence impedance characteristics of the double-circuit line under actual operating conditions.

[0112] In one possible embodiment, inputting the difference parameters and the historical zero-sequence running data into the physical twin model to obtain the digital twin model specifically includes the following steps:

[0113] B1. Determine the zero-sequence voltage difference parameter and the zero-sequence current difference parameter among the difference parameters;

[0114] B2. Determine the historical zero-sequence current data and historical zero-sequence voltage data in the historical operating data;

[0115] B3. Adjust the historical zero-sequence current data and the historical zero-sequence voltage data according to the zero-sequence voltage difference parameter and the zero-sequence current difference parameter respectively to obtain the target zero-sequence current data and the target zero-sequence voltage data.

[0116] B4. The physical twin model is trained based on the target zero-sequence current data and the target zero-sequence voltage data to obtain the digital twin model.

[0117] The difference parameter is the result of comparing historical operating data with simulation data. Historical zero-sequence voltage data includes the zero-sequence voltage amplitude and phase information measured at each node at different time periods, while historical zero-sequence current data includes the zero-sequence current amplitude and phase information of the corresponding node.

[0118] Specifically, by comparing and analyzing the deviations between the predicted and actual measured values ​​of historical zero-sequence voltage and current in the physical twin model, the zero-sequence voltage and current differences for each node and branch are calculated. Furthermore, the zero-sequence voltage and current difference parameters are determined. These difference parameters reflect the impact of historical environmental factors, line aging, capacitive current, and other external disturbances on the zero-sequence characteristics, enabling the digital twin model to more closely approximate the actual operating state on a physical basis. Next, historical zero-sequence current and voltage data are extracted from historical operating data. Through the organization and calibration of historical zero-sequence operating data, the system can form a complete data matrix describing the zero-sequence electrical characteristics of the line under historical operating conditions. Then, the historical zero-sequence current and voltage data are adjusted according to the zero-sequence voltage and current difference parameters to obtain the target zero-sequence current and voltage data. Specifically, the difference parameters are applied to historical data, and mathematical mapping methods such as amplitude correction and phase correction are used to correct the historical data, making it closer to the actual zero-sequence response characteristics. The adjusted target zero-sequence voltage and current data retain historical operating patterns while introducing correction information for deviations between historical data and simulations, providing more accurate training input for the physical twin model and achieving data-driven model optimization. Finally, the physical twin model is trained based on the target zero-sequence current and voltage data to obtain the digital twin model. During training, a hybrid intelligent approach, including a combination of deep learning and evolutionary algorithms, is used in the AI ​​analysis module to iteratively optimize the parameters of the physical twin model, enabling the model to adapt to both historical operating characteristics and changes in environmental factors. After training, the digital twin model not only possesses the structure and multiphysics characteristics of the physical twin model, but also continuously optimizes its parameters through self-learning, achieving accurate prediction and dynamic adaptation of zero-sequence impedance.

[0119] As can be seen, the constructed digital twin model can accurately map the zero-sequence impedance characteristics of a double-circuit line under actual operating conditions, enabling a comprehensive analysis of environmental factors, historical operating status, and line physical characteristics. Compared with traditional impedance analysis methods based on network equations, the embodiments of this application can dynamically adjust model parameters, improve the accuracy of zero-sequence impedance prediction, and maintain high reliability and adaptability under different operating conditions.

[0120] In one possible embodiment, the digital twin model includes a digital simulation module and an optimization module. The step of inputting the environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system specifically includes the following steps:

[0121] 351. Map the environmental data to the environmental parameter nodes of the digital twin model to obtain the first twin model;

[0122] 352. Input the line structure data into the topology nodes of the first twin model to obtain the second twin model;

[0123] 353. Input the zero-sequence voltage data and the zero-sequence current data into the second twin model to obtain the first zero-sequence impedance parameter;

[0124] 354. The simulation module generates a second zero-sequence impedance parameter based on the zero-sequence voltage data and the zero-sequence current data;

[0125] 355. The optimization module optimizes the first zero-sequence impedance parameter based on the second zero-sequence impedance parameter to obtain the zero-sequence impedance parameter.

[0126] The line structure data includes key parameters such as line length, conductor cross-section, conductor material, tower spacing, and the relative positions of phases and ground wires. By inputting this data into the topology nodes, the digital twin model can form a complete line topology and spatial geometry, providing a physical basis for the calculation of zero-sequence impedance. Zero-sequence voltage and zero-sequence current data are collected through a distributed intelligent sensor network, including zero-sequence response signals from the beginning and end of the double-circuit line.

[0127] Specifically, firstly, environmental data is mapped to the environmental parameter nodes of the digital twin model to obtain the first twin model. Environmental data includes information such as temperature, humidity, wind speed, and surrounding electromagnetic interference. By mapping the environmental parameter nodes, actual environmental conditions are introduced into the digital twin model, enabling it to realistically reflect the physical behavior and electrical characteristics of the distribution network lines under current external conditions. Next, line structure data is input into the topology nodes of the first twin model to obtain the second twin model. Zero-sequence voltage and current data are then input into the second twin model to obtain the first zero-sequence impedance parameter. Based on the zero-sequence voltage and current data, the second zero-sequence impedance parameter is generated. The simulation module constructs a mathematical model based on power system theory and the electromagnetic characteristics of the lines. Independent of the calculation process of the first zero-sequence impedance parameter, it simulates the zero-sequence voltage and current responses of a double-circuit line under different environmental conditions and outputs the predicted value of the zero-sequence impedance. This step provides a simulation reference that corroborates the first zero-sequence impedance parameter, providing a basis for correction by the optimization module. Finally, through the optimization module, the system optimizes the first zero-sequence impedance parameter based on the second zero-sequence impedance parameter to obtain the final zero-sequence impedance parameter. The optimization module iteratively compares the simulated second zero-sequence impedance with the initially measured first zero-sequence impedance through algorithmic comparison, and adaptively adjusts the model parameters to eliminate errors caused by measurement noise and environmental disturbances, achieving accurate optimization of the zero-sequence impedance parameter. The final zero-sequence impedance parameter not only includes actual measurement information but has also been verified and optimized through simulation, enabling it to accurately reflect the zero-sequence characteristics of a double-circuit distribution network.

[0128] As can be seen, the process first maps environmental and line structure data to the model, forming a twin model that considers environmental factors and line topology. Then, preliminary impedance parameters are obtained by combining actual zero-sequence voltage and current data. Secondary impedance parameters are generated through a simulation module. Finally, the optimization module iteratively optimizes the preliminary parameters to obtain the final high-precision zero-sequence impedance parameters. This achieves high-precision online monitoring of the zero-sequence impedance of a double-circuit line. Compared to traditional analysis methods based on zero-sequence network equations, this embodiment has advantages such as adaptability, strong real-time performance, and high error correction capability, effectively supporting line protection setting, fault diagnosis, and safe operation of the power grid.

[0129] For easier understanding, please refer to Figure 6 , Figure 6This is a flowchart illustrating another zero-sequence impedance detection method based on digital twins provided in this application embodiment. As can be seen, firstly, in step S1, a sweep frequency excitation signal with a continuously varying frequency within a preset frequency band is applied to the beginning of the de-energized line in a double-circuit line. The frequency range of this sweep frequency excitation signal is 40Hz to 60Hz, and the frequency change method is either continuous linear sweep or adaptive step sweep. Continuous linear sweep refers to the frequency increasing linearly from 40Hz to 60Hz at a constant rate; adaptive step sweep dynamically adjusts the frequency step size according to the system response characteristics, using a smaller frequency step size near key frequency points to obtain more accurate response data. Next, in step S2, a distributed intelligent sensor network deployed at both ends of the double-circuit line synchronously collects the zero-sequence voltage and zero-sequence current response data induced by the sweep frequency excitation signal on the operating line. Each intelligent sensor node in the distributed intelligent sensor network integrates a microprocessor for local preprocessing and feature extraction of the collected zero-sequence voltage and current data, and then uploads the processed data. The preprocessing performed by these microprocessors includes data filtering, outlier detection, and Fourier transform, which can significantly reduce transmission bandwidth requirements and improve data quality. The intelligent sensing nodes employ high-precision clock synchronization technology to ensure sampling time errors are controlled at the microsecond level, guaranteeing data temporal consistency. Then, step S3 is executed, where the zero-sequence voltage response data and zero-sequence current response data are input in real-time to a pre-constructed digital twin model of the line. This digital twin model is a multi-physics coupled electromagnetic transient model constructed based on line structural parameters, spatial relative position, ambient temperature, humidity, and historical operating data. This model not only considers the electrical characteristics of the line but also includes the coupling effects of multiple physical fields such as thermodynamics and mechanical stress, accurately reflecting the impact of external environmental changes on the line's zero-sequence impedance. The model obtains environmental parameters through a real-time meteorological data interface and dynamically adjusts calculation parameters to improve simulation accuracy. Finally, in step S4, the AI ​​analysis module processes the zero-sequence voltage response data and zero-sequence current response data, dynamically identifying and directly outputting the zero-sequence self-impedance and zero-sequence mutual impedance of the dual-loop line at power frequency. The AI ​​analysis module employs a trained deep learning network. The input to this network is swept frequency response data, and the output is the power frequency zero-sequence impedance parameter. A digital twin model compares the output of the AI ​​analysis module with the simulation results in real time, and dynamically adjusts the model parameters and / or optimizes the swept frequency excitation strategy based on the comparison results. The deep learning network uses a hybrid architecture combining convolutional neural networks and long short-term memory networks, enabling it to simultaneously handle frequency domain features and temporal variation characteristics, thus improving identification accuracy. Preferably, this method is periodically triggered during the normal operation of the double-circuit line, or triggered by the power grid dispatching system based on changes in operating status, to achieve online monitoring and trend analysis of zero-sequence mutual impedance. The time interval for periodic triggering can be adjusted according to the importance of the line and the frequency of environmental changes, typically every 4 hours or every 12 hours.Event triggers include, but are not limited to, the following: sudden changes in line load exceeding 20% ​​of rated capacity, ambient temperature changes exceeding 10°C, humidity changes exceeding 30%, lightning activity, and faults in adjacent lines. The system will also record monitoring results to generate a zero-sequence mutual impedance trend chart for predictive analysis and maintenance decision support.

[0130] visible, Figure 6 The described method can acquire the zero-sequence mutual impedance parameters of a double-circuit line in real time without affecting the normal operation of the power system. This provides accurate data support for power grid protection settings, fault analysis, and operation and maintenance, improving the safety and reliability of power grid operation. Compared with traditional offline measurement methods, this method avoids power outages caused by power outage testing, significantly improving measurement efficiency and data timeliness. Furthermore, through digital twin technology and AI analysis, it enhances measurement accuracy and anti-interference capabilities.

[0131] As can be seen, the above-mentioned zero-sequence impedance detection method based on digital twins achieves the mapping between the physical entity and virtual model of power equipment by integrating digital twin models of temperature, humidity, and line structure parameters, thereby reducing the interference of environmental factors on detection accuracy. In addition, by applying a variable frequency sweep excitation signal and collecting response data, the method replaces the construction and solution of equations, effectively reducing the interference caused by factors such as capacitance current, thereby improving the impedance detection accuracy.

[0132] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0134] When dividing each function into modules according to its corresponding function. Figure 7 This is a functional block diagram of a zero-sequence impedance detection device based on digital twins provided in this application embodiment. The zero-sequence impedance detection device 700 based on digital twins is applied to the main server of a distribution network system. The distribution network management system further includes: a detection module disposed at the beginning and end of a dual-circuit in the distribution network system; and a frequency sweep excitation module communicatively connected to the main server, with the output terminal of the frequency sweep excitation module connected to the beginning of the dual-circuit in the distribution network system. The device includes:

[0135] Acquisition unit 710 is used to acquire environmental data and line structure data of the power distribution network system;

[0136] The control unit 720 is used to control the frequency sweep excitation module to send a frequency sweep excitation signal with a preset frequency band to the power distribution network system; and to control the detection module to detect the zero-sequence detection signal of the power distribution network system in response to the frequency sweep excitation signal, and to send the zero-sequence detection signal to the main server.

[0137] The determining unit 730 is used to receive the zero-sequence detection signal and determine the zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal.

[0138] The calculation unit 740 is used to input the environmental data, the line structure data, the zero-sequence voltage data and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system.

[0139] In one possible embodiment, the detection module includes a sensor and a communication module, and the control unit 720 is specifically configured to: control the detection module to detect the zero-sequence detection signal of the power distribution system in response to the frequency sweep excitation signal;

[0140] The sensor detects the first response signal at the beginning of the dual-circuit of the power distribution network system and the second response signal at the end of the dual-circuit of the power distribution network system.

[0141] The first detection signal is determined based on the first response signal and the second response signal;

[0142] An anomaly signal is obtained by performing anomaly detection on the first detection signal based on a preset anomaly detection algorithm;

[0143] A second detection signal is determined based on the first detection signal and the abnormal signal;

[0144] The second detection signal is filtered based on a preset filtering algorithm to obtain a filtered detection signal;

[0145] Perform a Fourier transform on the filtered detection signal to obtain a zero-sequence detection signal;

[0146] The zero-sequence detection signal is sent to the main server via the communication module.

[0147] In one possible embodiment, the determining unit 730, in determining the zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal, is specifically configured to:

[0148] The zero-sequence detection signal is normalized to obtain a normalized detection signal;

[0149] The zero-sequence voltage signal is obtained by extracting the signal within a preset voltage frequency band from the normalized detection signal;

[0150] The zero-sequence current signal is obtained by extracting the signal within a preset current frequency band from the normalized detection signal.

[0151] The zero-sequence voltage signal and the zero-sequence current signal are demodulated respectively to obtain zero-sequence voltage amplitude data, zero-sequence voltage phase data, zero-sequence current amplitude data, and zero-sequence current phase data;

[0152] The zero-sequence voltage data is determined based on the zero-sequence voltage amplitude data and the zero-sequence voltage phase data;

[0153] The zero-sequence current data is determined based on the zero-sequence current amplitude data and the zero-sequence current phase data.

[0154] In one possible embodiment, before inputting the environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system, the determining unit 730 is further configured to:

[0155] Obtain historical operating data of the power distribution network system;

[0156] Identify the historical environmental data and historical zero-sequence operational data within the historical operational data;

[0157] Based on the line structure data, the historical environment data, and the historical zero-sequence operation data, a physical twin model is constructed and coupled with the physical model to obtain the physical twin model.

[0158] The historical operating data is input into a preset simulation model to obtain simulation data;

[0159] The simulation data and the historical operation data are compared to obtain the comparison results.

[0160] Determine the difference parameters based on the comparison results;

[0161] The difference parameters and the historical zero-sequence running data are input into the physical twin model to obtain the digital twin model.

[0162] In one possible embodiment, the determining unit 730, in constructing a physically coupled twin model based on the line structure data, the historical environment data, and the historical zero-sequence operation data to obtain a physical twin model, is specifically used for:

[0163] The physical topology and spatial location data of the distribution network system are determined based on the line structure data.

[0164] The historical zero-sequence runtime data, the historical environment data, and the physical topology are coupled to obtain a physical coupling dataset.

[0165] The physical coupling model is generated based on the physical coupling dataset and the spatial location data.

[0166] In one possible embodiment, the determining unit 730, in the aspect of inputting the difference parameter and the historical zero-sequence running data into the physical twin model to obtain the digital twin model, is specifically used for:

[0167] Determine the zero-sequence voltage difference parameter and the zero-sequence current difference parameter among the difference parameters;

[0168] Determine the historical zero-sequence current data and historical zero-sequence voltage data from the historical operating data;

[0169] The historical zero-sequence current data and the historical zero-sequence voltage data are adjusted according to the zero-sequence voltage difference parameter and the zero-sequence current difference parameter, respectively, to obtain the target zero-sequence current data and the target zero-sequence voltage data.

[0170] The physical twin model is trained based on the target zero-sequence current data and the target zero-sequence voltage data to obtain the digital twin model.

[0171] In one possible embodiment, the digital twin model includes a digital simulation module and an optimization module. The computing unit 740, in the process of inputting the environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system, is specifically used for:

[0172] The environmental data is mapped to the environmental parameter nodes of the digital twin model to obtain the first twin model;

[0173] The line structure data is input into the topology nodes of the first twin model to obtain the second twin model;

[0174] The zero-sequence voltage data and the zero-sequence current data are input into the second twin model to obtain the first zero-sequence impedance parameter;

[0175] The simulation module generates a second zero-sequence impedance parameter based on the zero-sequence voltage data and the zero-sequence current data.

[0176] The optimization module optimizes the first zero-sequence impedance parameter based on the second zero-sequence impedance parameter to obtain the zero-sequence impedance parameter.

[0177] As can be seen, the zero-sequence impedance detection device based on digital twin described in this application involves: acquiring environmental data and line structure data of the distribution network system; controlling a frequency sweep excitation module to send a frequency sweep excitation signal varying within a preset frequency band to the distribution network system; controlling a detection module to detect the zero-sequence detection signal of the distribution network system in response to the frequency sweep excitation signal; receiving the zero-sequence detection signal; determining zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal; and inputting the environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters. This can improve the impedance detection accuracy in dual-circuit lines.

[0178] It should be noted that the specific implementation of each operation can be described in the corresponding description of the method embodiment shown above. The zero-sequence impedance detection device 700 based on digital twin can be used to execute the zero-sequence impedance detection method embodiment based on digital twin of this application, and will not be described again here.

[0179] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0180] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0181] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0182] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0183] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0184] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Additionally, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0185] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0186] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0187] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A zero-sequence impedance detection method based on digital twin, characterized in that, A main server is applied to a power distribution network system, the power distribution network system further comprising: a detection module disposed on a first end and a second end of a transmission line in the power distribution network system, the first end being the current input position of the transmission line and the second end being the current output position of the transmission line; a frequency sweep excitation module communicatively connected to the main server, and the frequency sweep excitation module being electrically connected to the first end, the method comprising: Obtain environmental data and line structure data of the power distribution network system; The frequency sweep excitation module is controlled to send a frequency sweep excitation signal with a preset frequency band to the power distribution network system; The detection module is controlled to detect the zero-sequence detection signal of the power distribution system in response to the frequency sweep excitation signal; Receive the zero-sequence detection signal and determine the zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal; The environmental data, the line structure data, the zero-sequence voltage data, and the zero-sequence current data are input into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system. The method further includes, before inputting the environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system: Obtain historical operating data of the power distribution network system; Identify the historical environmental data and historical zero-sequence operational data within the historical operational data; Based on the line structure data, the historical environment data, and the historical zero-sequence operation data, a physical twin model is constructed and coupled with the physical model to obtain the physical twin model. The historical operating data is input into a preset simulation model to obtain simulation data; The simulation data and the historical operation data are compared to obtain the comparison results. Determine the difference parameters based on the comparison results; The difference parameters and the historical zero-sequence running data are input into the physical twin model to obtain the digital twin model.

2. The method as described in claim 1, characterized in that, The detection module includes a sensor and a communication module. Controlling the detection module to detect the zero-sequence detection signal of the power distribution system in response to the frequency sweep excitation signal includes: The sensor detects the first response signal at the beginning of the dual-circuit of the power distribution network system and the second response signal at the end of the dual-circuit of the power distribution network system. The first detection signal is determined based on the first response signal and the second response signal; An anomaly signal is obtained by performing anomaly detection on the first detection signal based on a preset anomaly detection algorithm; A second detection signal is determined based on the first detection signal and the abnormal signal; The second detection signal is filtered based on a preset filtering algorithm to obtain a filtered detection signal; Perform a Fourier transform on the filtered detection signal to obtain a zero-sequence detection signal; The zero-sequence detection signal is sent to the main server via the communication module.

3. The method as described in claim 1, characterized in that, The step of determining zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal includes: The zero-sequence detection signal is normalized to obtain a normalized detection signal; The zero-sequence voltage signal is obtained by extracting the signal within a preset voltage frequency band from the normalized detection signal; The zero-sequence current signal is obtained by extracting the signal within a preset current frequency band from the normalized detection signal. The zero-sequence voltage signal and the zero-sequence current signal are demodulated respectively to obtain zero-sequence voltage amplitude data, zero-sequence voltage phase data, zero-sequence current amplitude data, and zero-sequence current phase data; The zero-sequence voltage data is determined based on the zero-sequence voltage amplitude data and the zero-sequence voltage phase data; The zero-sequence current data is determined based on the zero-sequence current amplitude data and the zero-sequence current phase data.

4. The method as described in claim 1, characterized in that, The step of constructing a physical twin model based on the line structure data, the historical environmental data, and the historical zero-sequence operation data, to obtain a physical twin model, includes: The physical topology and spatial location data of the distribution network system are determined based on the line structure data. The historical zero-sequence runtime data, the historical environment data, and the physical topology are coupled to obtain a physical coupling dataset. The physical twin model is generated based on the physical coupling dataset and the spatial location data.

5. The method as described in claim 1, characterized in that, The step of inputting the difference parameters and the historical zero-sequence running data into the physical twin model to obtain the digital twin model includes: Determine the zero-sequence voltage difference parameter and the zero-sequence current difference parameter among the difference parameters; Determine the historical zero-sequence current data and historical zero-sequence voltage data from the historical operating data; The historical zero-sequence current data and the historical zero-sequence voltage data are adjusted according to the zero-sequence voltage difference parameter and the zero-sequence current difference parameter, respectively, to obtain the target zero-sequence current data and the target zero-sequence voltage data. The physical twin model is trained based on the target zero-sequence current data and the target zero-sequence voltage data to obtain the digital twin model.

6. The method as described in claim 1, characterized in that, The digital twin model includes a digital simulation module and an optimization module. The environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data are input into the preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system, including: The environmental data is mapped to the environmental parameter nodes of the digital twin model to obtain the first twin model; The line structure data is input into the topology nodes of the first twin model to obtain the second twin model; The zero-sequence voltage data and the zero-sequence current data are input into the second twin model to obtain the first zero-sequence impedance parameter; The simulation module generates a second zero-sequence impedance parameter based on the zero-sequence voltage data and the zero-sequence current data. The optimization module optimizes the first zero-sequence impedance parameter based on the second zero-sequence impedance parameter to obtain the zero-sequence impedance parameter.

7. A zero-sequence impedance detection device based on digital twin, characterized in that, A main server is applied to a power distribution network system, the power distribution network system further comprising: a detection module disposed at the beginning and end of a dual-circuit in the power distribution network system; a frequency sweep excitation module communicatively connected to the main server, wherein the output terminal of the frequency sweep excitation module is connected to the beginning of the dual-circuit in the power distribution network system, the device comprising: The acquisition unit is used to acquire environmental data and line structure data of the power distribution network system; The control unit is used to control the frequency sweeping excitation module to send a frequency sweeping excitation signal with a preset frequency band to the power distribution network system; and to control the detection module to detect the zero-sequence detection signal of the power distribution network system in response to the frequency sweeping excitation signal, and send the zero-sequence detection signal to the main server. A determining unit is configured to receive the zero-sequence detection signal and determine zero-sequence voltage data and zero-sequence current data based on the zero-sequence detection signal; The calculation unit is used to input the environmental data, the line structure data, the zero-sequence voltage data and the zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system. The method further includes, before inputting the environmental data, line structure data, zero-sequence voltage data, and zero-sequence current data into a preset digital twin model to obtain the zero-sequence impedance parameters of the distribution network system: Obtain historical operating data of the power distribution network system; Identify the historical environmental data and historical zero-sequence operational data within the historical operational data; Based on the line structure data, the historical environment data, and the historical zero-sequence operation data, a physical twin model is constructed and coupled with the physical model to obtain the physical twin model. The historical operating data is input into a preset simulation model to obtain simulation data; The simulation data and the historical operation data are compared to obtain the comparison results. Determine the difference parameters based on the comparison results; The difference parameters and the historical zero-sequence running data are input into the physical twin model to obtain the digital twin model.

8. A server, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-6.

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